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author | Rémi Flamary <remi.flamary@gmail.com> | 2021-10-27 08:41:08 +0200 |
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committer | GitHub <noreply@github.com> | 2021-10-27 08:41:08 +0200 |
commit | d7554331fc409fea48ee758fd630909dd9dc4827 (patch) | |
tree | 9b8ed4bf94c12d034d5fb1de5b7b5b76c23b4d05 /README.md | |
parent | 76450dddf8dd62b9714b72e99ae075516246d433 (diff) |
[WIP] Sinkhorn in log space (#290)
* adda sinkhorn log and working sinkhorn2 function
* more tests pass
* more tests pass
* it works but not by default yet
* remove warningd
* update circleci doc
* update circleci doc
* new sinkhorn implemeted but not by default
* better
* doctest pass
* test doctest
* new test utils
* remove pep8 errors
* remove pep8 errors
* doc new implementtaion with log
* test sinkhorn 2
* doc for log implementation
Diffstat (limited to 'README.md')
-rw-r--r-- | README.md | 4 |
1 files changed, 3 insertions, 1 deletions
@@ -20,7 +20,7 @@ POT provides the following generic OT solvers (links to examples): * [OT Network Simplex solver](https://pythonot.github.io/auto_examples/plot_OT_1D.html) for the linear program/ Earth Movers Distance [1] . * [Conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) [6] and [Generalized conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) for regularized OT [7]. -* Entropic regularization OT solver with [Sinkhorn Knopp Algorithm](https://pythonot.github.io/auto_examples/plot_OT_1D.html) [2] , stabilized version [9] [10], greedy Sinkhorn [22] and [Screening Sinkhorn [26] ](https://pythonot.github.io/auto_examples/plot_screenkhorn_1D.html). +* Entropic regularization OT solver with [Sinkhorn Knopp Algorithm](https://pythonot.github.io/auto_examples/plot_OT_1D.html) [2] , stabilized version [9] [10] [34], greedy Sinkhorn [22] and [Screening Sinkhorn [26] ](https://pythonot.github.io/auto_examples/plot_screenkhorn_1D.html). * Bregman projections for [Wasserstein barycenter](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html) [3], [convolutional barycenter](https://pythonot.github.io/auto_examples/barycenters/plot_convolutional_barycenter.html) [21] and unmixing [4]. * Sinkhorn divergence [23] and entropic regularization OT from empirical data. * [Smooth optimal transport solvers](https://pythonot.github.io/auto_examples/plot_OT_1D_smooth.html) (dual and semi-dual) for KL and squared L2 regularizations [17]. @@ -290,3 +290,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t [32] Huang, M., Ma S., Lai, L. (2021). [A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance](http://proceedings.mlr.press/v139/huang21e.html), Proceedings of the 38th International Conference on Machine Learning (ICML). [33] Kerdoncuff T., Emonet R., Marc S. [Sampled Gromov Wasserstein](https://hal.archives-ouvertes.fr/hal-03232509/document), Machine Learning Journal (MJL), 2021 + +[34] Feydy, J., Séjourné, T., Vialard, F. X., Amari, S. I., Trouvé, A., & Peyré, G. (2019, April). Interpolating between optimal transport and MMD using Sinkhorn divergences. In The 22nd International Conference on Artificial Intelligence and Statistics (pp. 2681-2690). PMLR. |